Abstract
Case-based reasoning (CBR) is frequently applied to data mining with various objectives. Unfortunately, it suffers from the feature weighting problem. In this framework, similar case retrieval plays an important role, and the k-nearest neighbor (k-nn) method or its variants are widely used as the retrieval mechanism. However, the most important assumption of k-nn is that all of the features presented are equally important, which is not true in many practical applications. Many variants of k-nn have been proposed to assign higher weights to the more relevant features for case retrieval. Though many feature-weighted variants of k-nn have been reported to improve its retrieval accuracy on some tasks, few have been used in conjunction with the neural network learning. We propose CANSY, a feature-weighted CBR with neural network for symbolic features.
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Park, S.C., Kim, J.W., Im, K.H. (2006). Feature-Weighted CBR with Neural Network for Symbolic Features. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_123
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DOI: https://doi.org/10.1007/11816157_123
Publisher Name: Springer, Berlin, Heidelberg
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